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Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study.

, , and . Inf. Process. Manag., 57 (6): 102360 (2020)

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Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media., , and . LREC, page 6237-6246. European Language Resources Association, (2020)Automatic Identification of Misogyny in English and Italian Tweets at EVALITA 2018 with a Multilingual Hate Lexicon., , , and . EVALITA@CLiC-it, volume 2263 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)Clustering of ERP business process fragments., , , and . IC3INA, page 319-324. IEEE, (2013)Hate Speech Detection on Indonesian Social Media: A Preliminary Study on Code-Mixed Language Issue., , and . NLPIR, page 104-109. ACM, (2022)An empirical study of software ecosystem related tweets by npm maintainers., , , , , , , , and . PeerJ Comput. Sci., (2024)Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study., , and . Inf. Process. Manag., 57 (6): 102360 (2020)A joint learning approach with knowledge injection for zero-shot cross-lingual hate speech detection., , and . Inf. Process. Manag., 58 (4): 102544 (2021)Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure., , and . CIKM Workshops, volume 2482 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language., , , and . WOAH, page 34-43. Association for Computational Linguistics, (2020)#NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets., and . SemEval@NAACL-HLT, page 649-654. Association for Computational Linguistics, (2018)